COMPARING AI-ASSISTED AND TRADITIONAL TEACHING IN COLLEGE ENGLISH: PEDAGOGICAL BENEFITS AND LEARNING BEHAVIORS

Aim — to identify practical differences between AI-assisted and traditional college English teaching by examining pedagogical benefits and student learning behaviors within one comparative framework. The problem arises because the balance among individualized feedback, teacher mediation, and independent practice is not maintained equally across instructional modes. Methods — a three-stage pedagogical experiment was conducted through a diagnostic pretest, formative intervention, and control posttest; the study used a quasi-experimental pretest-posttest design with 60 students in the AI-assisted teaching group and 60 students in the traditional teaching group. Internal reliability of the measurement instrument was examined with Cronbach’s alpha, while between-group outcomes were evaluated through descriptive statistics and comparative analysis. Results — the baseline achievement score was M = 60.4, SD = 8.1, with a range of 42–78; among 120 participants, 38 students, or 31.7 percent, reported regular AI-supported practice, 34 students (28.3 percent) independently reviewed textbook or lecture material, 28 students (23.3 percent) studied collaboratively with peers, and 20 students (16.7 percent) used a mixed strategy. Conclusion — structured AI-assisted teaching broadened pedagogical benefits and supported more active forms of learning behavior. The scientific contribution lies in combining instructional mode, pedagogical benefits, and behavior within a three-stage, theory-informed comparative model; practically, the findings support purposeful AI integration while retaining teacher oversight.